Big Data Analytics on AWS - Carlos Conde - AWS Summit Paris

•

4 j'aime•1,548 vues

This document discusses using AWS for big data analytics. It notes that as data volumes grow, collecting, storing, analyzing and sharing data is essential. AWS provides services like S3, DynamoDB and EMR on Hadoop to help with collecting, storing, analyzing and sharing large volumes of data cost effectively. It also discusses using tools like Pig and parallelizing workloads to analyze data more efficiently.

Technologie Business

BIG DATA ANALYTICS
WITH AWS
Carlos Conde │Solutions Architect

BIG DATA
When innovation is required to collect,
store, analyze, and share your data

27 TB per day
Large Hadron Collider – CERN

The more data you collect

The more VALUE you
can derive from it

Big Data Verticals

Social
Media/Advertising Oil & Gas Retail Life Sciences Financial Services Security
Network/Gaming

User
Monte
Anti-virus Demographics
Targeted Recommendations Carlo
Advertising Simulations

Seismic Genome Fraud Usage
Analysis Analysis Detection analysis

Image and
Transaction Risk
Video Analysis Analysis Image In-game
Processing
Recognition metrics

 2.7 Zetabytes in
2012

 Over 90% will be
unstructured

CLUSTER GPU
QUADRUPLE EXTRA LARGE
Intel Xeon X5570, quad-core
2x Nehalem architecture
NVIDIA Tesla Fermi
2x M2050 GPUs
22 GB of memory – 1.7 TB of storage

$2.1 PER HOUR

ON A SINGLE INSTANCE

COST: 4h x $2.1 = $8.4
RENDERING TIME: 4h

ON MULTIPLE INSTANCES

COST: 2 x 2h x $2.1 = $8.4
RENDERING TIME:

"Hadoop is a reliable storage and data analysis system"

HDFS MapReduce

Deploying a Hadoop cluster is hard

http://eddie.niese.net/20090313/dont-pity-incompetence/

USE THE RIGHT TOOL
FOR THE RIGHT JOB

RDBMS Hadoop

 Interactive Reporting  Affordable
(<1sec) Storage/Compute
 Multistep Transactions  Structured or Not (Agility)
 Lots of Updates/Deletes  Resilient Auto Scalability

Data Warehouse
(Batch Processing)
Data Warehouse Data Warehouse
(Steady State) (Steady State)

Shrink to
Expand to 9 instances
25 instances

OPERATIONAL HADOOP EXPERIENCE
Operated over a million Hadoop clusters last year

PUBLIC
DATA SETS
http://aws.amazon.com/publicdatasets

« Want to increase innovation?
Lower the cost of failure »

Joi Ito

AWS LOWERS THE COST
OF INNOVATION
Testing a new idea is cheap

Big Data Analytics on AWS - Carlos Conde - AWS Summit Paris

Contenu connexe

Tendances

Collecting, maintaining, and analyzing data is key to keeping pace within any industry today. In addition to being a critical competitive asset, maintaining corporate data requires careful foundational planning to ensure that the data is secure at all stages. Your big data may include not only proprietary non-public information, but also controlled data that must adhere to regulations such as HIPAA or ITAR. Securing this data while maintaining access for authorized data analytics and reporting workloads can pose significant challenges. In this talk, you’ll learn about strategies leveraging tools such as AWS Identity and Access Management (IAM), AWS Key Management Service (KMS) , Amazon S3, and Amazon EMR to secure your big data workloads in the cloud. Level: 200 Speaker: Hannah Marlowe - Consultant, Federal, WWPS Professional Services

Securing Your Big Data on AWS

Amazon Web Services

Amazon big success using big data analytics

Kovid Academy

This overview presentation discusses big data challenges and provides an overview of the AWS Big Data Platform by covering: - How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs. - Reference architectures for popular use cases, including, connected devices (IoT), log streaming, real-time intelligence, and analytics. - The AWS big data portfolio of services, including, Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR), and Redshift. - The latest relational database engine, Amazon Aurora— a MySQL-compatible, highly-available relational database engine, which provides up to five times better performance than MySQL at one-tenth the cost of a commercial database. Created by: Rahul Pathak, Sr. Manager of Software Development

AWS Big Data Platform

Amazon Web Services

Eventually Everything Connects

Amazon Web Services

AWS & Database Analytics

Amazon Web Services

Many organisations find it challenging to successfully perform real-time data analytics using their own on premise IT infrastructure. Building a system that can adapt and scale rapidly to handle dramatic increases in transaction loads can potentially be quite a costly and time consuming exercise. Most of the time, infrastructure is under-utilised and it’s near impossible for organisations to forecast the amount of computing power they will need in the future to serve their customers and suppliers. To overcome these challenges, organisations can instead utilise the cloud to support their real-time data analytics activities. Scalable, agile and secure, cloud-based infrastructure enables organisations to quickly spin up infrastructure to support their data analytics projects exactly when it is needed. Importantly, they can ‘switch off’ infrastructure when it is not. BluePi Consulting and Amazon Web Services (AWS) are giving you the opportunity to discover how organisations are using real time data analytics to gain new insights from their information to improve the customer experience and drive competitive advantage.

Using real time big data analytics for competitive advantage

Amazon Web Services

Big Data, Analytics, and Content Recommendations on AWS

Amazon Web Services

Building a Big Data & Analytics Platform using AWS

Amazon Web Services

Data analysis is being used to transform businesses, increase efficiency, and drive innovation. But organizations need to perform increasingly complex analysis on their data (streaming analytics, ad-hoc querying and predictive analytics) in order to get better insights and actionable business intelligence. The growing data volume, speed, and complexity of diverse data formats make legacy tools inadequate or difficult to use. The AWS Cloud has a comprehensive portfolio of analytics services to help you process data of any volume and automate how you put that data to work for your organization. In this session we’ll see how to put those services at work on structured, unstructured and real-time data.

Get Value from Your Data

Danilo Poccia

Keynote AWS Summit 2013 Sao Paulo

Amazon Web Services LATAM

16h00 globant - aws globant-big-data_summit2012

infolive

Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects. We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.

2016 AWS Big Data Solution Days

Amazon Web Services

Think Big Data, Think Cloud - AWS Presentation - AWS Cloud Storage for the En...

Amazon Web Services

Customers are adopting Apache Spark ‒ an open-source distributed processing framework ‒ on Amazon EMR for large-scale machine learning workloads, especially for applications that power customer segmentation and content recommendation. By leveraging Spark ML, a set of machine learning algorithms included with Spark, customers can quickly build and execute massively parallel machine learning jobs. Additionally, Spark applications can train models in streaming or batch contexts, and can access data from Amazon S3, Amazon Kinesis, Amazon Redshift, and other services. This session explains how to quickly and easily create scalable Spark clusters with Amazon EMR, build and share models using Apache Zeppelin and Jupyter notebooks, and use the Spark ML pipelines API to manage your training workflow. In addition, Jasjeet Thind, Senior Director of Data Science and Engineering at Zillow Group, will discuss his organization's development of personalization algorithms and platforms at scale using Spark on Amazon EMR.

AWS re:Invent 2016: Zillow Group: Developing Classification and Recommendatio...

Amazon Web Services

Amazon QuickSight is a fast BI service that makes it easy for you to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. QuickSight is built to harness the power and scalability of the cloud, so you can easily run analysis on large datasets, and support hundreds of thousands of users. In this session, we’ll demonstrate how you can easily get started with Amazon QuickSight, uploading files, connecting to S3 and Redshift and creating analyses from visualizations that are optimized based on the underlying data. Once we’ve built our analysis and dashboard, we’ll show you easy it is to share it with colleagues and stakeholders in just a few seconds. And with SPICE – QuckSight’s in-memory calculation engine – you can go from data to insights, faster than ever.

AWS re:Invent 2016: Visualizing Big Data Insights with Amazon QuickSight (BDM...

Amazon Web Services

Working with Amazon Web Services “AWS” and 1Strategy, an Advance AWS Consulting partner; the Cambia Health Data Sciences teams have been able to deploy HIPAA compliant and secured AWS Elastic Map Reduce (EMR) data pipelines on the cloud. In this session, we will dive deep into the architectural components of this solution and you will learn how utilizing AWS services has helped Cambia decrease processing time for analytics, increase application flexibility and accelerate speed to production. The second part of the session is going to cover machine learning and its role in reducing cost and improving quality of care. The healthcare community must rely on advanced analytics and machine learning to analyze multiple facets of healthcare data and process it at scale to gain insights on things that matter. You will learn why AWS is a well suited platform for machine learning. We will take you through the steps of building a machine learning model using Amazon ML for a real world problem of predicting patient readmissions.

AWS re:Invent 2016| HLC301 | Data Science and Healthcare: Running Large Scale...

Amazon Web Services

Applying Machine learning to IOT: End to End Distributed Distributed Pipeline...

Carol McDonald

2 years ago if someone had claimed they could stand up a petabyte scale data warehouse in under an hour and then have a non-technical business user querying it live 30 minutes later without knowing any SQL or coding language, they would have been laughed out of the room. These days, that’s called taking advantage of disruptive technology. Amazon Web Services and Tableau Software have shifted the entire paradigm by which organizations not only store and access their data, but ultimately how they innovate with it. The fast, scalable, and inexpensive services that AWS provides for housing data combined with Tableau’s unbelievably flexible and user friendly visual analytic solution means that within hours an organization can securely put the power of their massive data assets into the hands of their domain experts without expensive overhead or lengthy ramp-up time. Attend this webinar to learn how Amazon Web Services and Tableau Software are leveraged together everyday to: • Empower visual ad-hoc data discovery against big data • Revolutionize corporate reporting and dashboards • Promote data driven decision making at every level The presentation will include: • A live demonstration of AWS and Tableau working together • A real customer case study focused on fraud detection and online video metrics • Live Q&A and an opportunity to trial both solutions

AWS Webcast - Tableau Big Data Solution Showcase

Amazon Web Services

Aaum Analytics event - Big data in the cloud

Ganesh Raja

Big data on aws

Serkan Özal

Tendances (20)

Securing Your Big Data on AWS

Amazon big success using big data analytics

AWS Big Data Platform

Eventually Everything Connects

AWS & Database Analytics

Using real time big data analytics for competitive advantage

Big Data, Analytics, and Content Recommendations on AWS

Building a Big Data & Analytics Platform using AWS

Get Value from Your Data

Keynote AWS Summit 2013 Sao Paulo

16h00 globant - aws globant-big-data_summit2012

2016 AWS Big Data Solution Days

Think Big Data, Think Cloud - AWS Presentation - AWS Cloud Storage for the En...

AWS re:Invent 2016: Zillow Group: Developing Classification and Recommendatio...

AWS re:Invent 2016: Visualizing Big Data Insights with Amazon QuickSight (BDM...

AWS re:Invent 2016| HLC301 | Data Science and Healthcare: Running Large Scale...

Applying Machine learning to IOT: End to End Distributed Distributed Pipeline...

AWS Webcast - Tableau Big Data Solution Showcase

Aaum Analytics event - Big data in the cloud

Big data on aws

En vedette

Big Data Case Studies

UIResearchPark

Big Data Science Team Building

UIResearchPark

An exciting talk on the main difficulties and how to overcome them when building and scaling data teams with Florian Douetteau - Technological issues: What stack should they choose for the company’s architecture? And what about big data technologies; should they accept being a polyglot or rather assume being a ruthless dictator? - HR issues: Who should they hire? Should they build their data team as an extension of the BI team? Or should they build it from scratch? - Data issues: How are they supposed to get data inside his data lake? Which strategy should they adopt: the cicada, the spider or the fox one? - Product issues: What is big data really about? And eventually, what are they willing to do with this bunch of data? The talk aims at demonstrating how tough it can be to build and scale a data department, and at giving some insights about the strategy Florian thinks they should adopt.

Building & Scaling Data Teams

Outreach Digital

AI is all the rage in tech circles, and the press is awash in tales of AI entrepreneurs striking it rich after being acquired by one of the giants. As always, the realities of building a startup are different, and the path to success requires not just technical prowess but also thoughtful market positioning and business excellence. In a talk of interest to anyone building or implementing an AI product, Matt Turck and Peter Brodsky leverage hundreds of conversations with AI (and big data) founders and hard-learned lessons building companies from the ground up to highlight successful strategies and tactics. Topics include: Successful data acquisition strategies Data network effects Competing with the giants A pragmatic approach to building an AI team Why social engineering is just as important to success as groundbreaking AI technology

Building an AI Startup: Realities & Tactics

Matt Turck

Cahyo is a data geek, gamer and comic nerd. Excel and Database are his favorite since his middle school. Having graduated from a Vocational High School of Informatics and Technology made him able to start his career early and led many DWH BI projects at his early 20. He currently leading a data team in bizzy.co.id as the Head of Data Analytics. Previously he worked for Microsoft Indonesia as Data Platform Technology Specialist where he provides strategic technical leadership supporting Microsoft customers and partners to adopt, deploy, and support solutions based on SQL Server and Data Platform related technologies. *** This slide was shared at Tech in Asia Product Development Conference 2017 (PDC'17) on 9-10 August 2017. Get more insightful updates from TIA by subscribing techin.asia/updateselalu

"How To Build and Lead a Winning Data Team" by Cahyo Listyanto (Bizzy.co.id)

Tech in Asia ID

As you walk into your office on Monday morning, before you've even had a chance to grab a cup of coffee, your CEO asks to see you. He's worried: both customer churn and fraudulent transactions have increased over the past 6 months. As Data Manager, you have 6 months to solve that. As Data Manager, you know the challenges ahead: Multitudes of technology choices to make Building a team and solving the skill-set disconnect Data can be deceiving... Figuring out what the successful data product must be The goal of this talk is to provide some perspective to these topics Florian works in the “data” field since 01’, back when it was not yet big. He worked in successful startups in search engine, advertising and gaming industries, holding various data or CTO’s role. He started Dataiku in 2013, his first venture as a CEO, with the goal of alleviating the daily pains from the data enthusiasts and let them express their creativity.

How to Build a Successful Data Team - Florian Douetteau @ PAPIs Connect

PAPIs.io

The Machine Learning Canvas is a template for developing new (or documenting existing) intelligent systems based on data and machine learning. It is a visual chart with elements describing the key aspects of such systems: the value proposition, the data to learn from (to create predictive models), the utilization of predictions (to create proposed value), requirements and measures of performance. It assists teams of data scientists, software engineers, product and business managers, in aligning their activities. This tutorial will help you get into the right mindset to go beyond the current hype around machine learning, beyond proofs of concept, and to clearly see how this technology can have an actual impact in your domain. I’ll present the general structure of the Canvas, the different boxes it is composed of and the associated questions to answer. We’ll see how to fill it in iteratively on a churn prevention example.

From Data to AI with the Machine Learning Canvas

Louis Dorard

En vedette (7)

Big Data Case Studies

Big Data Science Team Building

Building & Scaling Data Teams

Building an AI Startup: Realities & Tactics

"How To Build and Lead a Winning Data Team" by Cahyo Listyanto (Bizzy.co.id)

How to Build a Successful Data Team - Florian Douetteau @ PAPIs Connect

From Data to AI with the Machine Learning Canvas

Similaire à Big Data Analytics on AWS - Carlos Conde - AWS Summit Paris

Big Data & The Cloud

Amazon Web Services

Big Data on AWS

Amazon Web Services LATAM

16h30 p duff-big-data-final

Luiz Gustavo Santos

Big data technologies let you work with any velocity, volume, or variety of data in a highly productive environment. This session seeks to answer questions such as "what is big data," "how can I use unstructured data," and "how can I integrate data collections from different sources" using Hadoop with Amazon Elastic MapReduce. Join general manager of EMR, Peter Sirota, on a journey through real-world use cases of data-driven discovery.

BDT101 Big Data with Amazon Elastic MapReduce - AWS re: Invent 2012

Amazon Web Services

Data Without Limit - Dr. Werner Vogels - AWS Summit 2012 Australia

Amazon Web Services

Etendez votre datacenter avec aws v4

Amazon Web Services

Extracting value from Big Data is not easy. The field of technologies and vendors is fragmented and rapidly evolving. End-to-end, general purpose solutions that work out of the box don’t exist yet, and Hadoop is no exception. And most companies lack Big Data specialists. The key to unlocking real value /// extracting the gold nuggets at the end of the rainbow (???) /// lies with mapping the business requirements smartly against the emerging and imperfect ecosystem of technology and vendor choices. There is a long list of crucial questions to think about. How fast is the data flying at you? Are your Big Data analyses tightly integrated with existing systems? Or parallel and complex? Can you tolerate a minute of latency? Do you accept data loss or generous SLAs? Is imperfect security good enough? The answer to Big Data ROI lies somewhere between the herd and nerd mentality. Thinking hard and being smart about each use case as early as possible avoids costly mistakes. This talk will illustrate how Deutsche Telekom follows this segmentation approach to make sure every individual use case drives architecture design and technology selection.

Don't be Hadooped when looking for Big Data ROI

DataWorks Summit

Nagios Conference 2012 - Dave Josephsen - 2002 called they want there rrd she...

Nagios

Architecting Virtualized Infrastructure for Big Data

Richard McDougall

Big Data and the Cloud a Best Friend Story

Amazon Web Services

Cetas Analytics as a Service for Predictive Analytics

J. David Morris

Cetas Predictive Analytics Prezo

Pivotal Analytics (Cetas Analytics)

Optimizing data mining process using graphic processors

Gurupad Hegde

IBM Smart Camp: Philippe Souidi on Big Data

Philippe Souidi

Hive + Amazon EMR + S3 = Elastic big data SQL analytics processing in the cloud

Jaipaul Agonus

Software Development Engineers Ireland

Sean O'Sullivan

Throw out everything that you know about security tools today. No more six-figure appliances that only do one thing marginally well. No more proprietary protocols. We deserve better and we demand better. Envision a world where your security tools talk with eachother. They communicate and share data in order to leverage eachothers strengths and and help compensate for their weaknesses. They work together to solve problems. Envision "Symbiotic Security". Symbiotic Security is a new term that was coined to describe the ability of a tool to consume data from other tools or provide data to other tools. As part of our research, we have examined various classes of tools on the market and identified these abilities in each of them resulting in a label of "Consumer", "Provider", or "Symbiotic". As a consumer of security tools, this completely revolutionizes the way that we make purchases. As an example, let's pretend that you are purchasing a new Intrusion Prevention System for your enterprise. As you begin to evaluate the various tools from the Gartner Magic Quadrant, you quickly realize that they almost all have the same primary feature set. The key differentiator at this point aren't the rules or the hardware, but rather, the ability for the system to send and receive data with other systems. The IPS itself has some signatures and blocking abilities, but has zero relevancy data. Now, we give the IPS the ability to pull in vulnerability data and system configuration information from network and host scans and we gain relevancy. Add in some additional data on where the potential threat is coming from and now you have the data necessary to take a decisive action on threats. This new system is a "Consumer". Now, if you give the IPS the ability to send information to other devices on things like the source of relevant threats, those devices, like a firewall or HIPS, can now make intelligent blocking decisions as well. Our IPS now has "Provider" abilities. Since our IPS is labeled as both a "Provider" and "Consumer" it is deemed "Symbiotic". This convention can now be used both by the manufacturer to market the value-add of the device as well as a way for the purchasers to differentiate between otherwise similar devices. In order to demonstrate the true powers of being symbiotic, we are releasing a free tool that epitomizes this concept. The tool, named ThreadFix, has been labeled as a "Consumer" because of it's abilities to pull vulnerability data from static and dynamic scanning tools, threat modeling, and manual penetration tests as well as alert logs and vulnerability details from IDS, IPS, and WAF products. ThreadFix has also been labeled as a "Provider" because of it's abilities to normalize the data consumed and pass it along to IDS, IPS, and WAF for action as well as to your bug tracking system for remediation tracking. Because it can serve both a consumer and provider role, we designate it as a "Symbiotic" tool.

The Magic of Symbiotic Security

Denim Group

Analytics in the Cloud

Amazon Web Services

Fred Oh's presentation for SNW Spring, Monday 4/2/12, 1:00–1:45PM Unstructured data growth is in an explosive state, and has no signs of slowing down. Costs continue to rise along with new regulations mandating longer data retention. Moreover, disparate silos, multivendor storage assets and less than optimal use of existing assets have all contributed to ‘accidental architectures.’ And while they can be key drivers for organizations to explore incremental, innovative solutions to their data challenges, they may provide only short-term gain. Join us for this session as we outline the business benefits of a truly unified, integrated platform to manage all block, file and object data that allows enterprises can make the most out of their storage resources. We explore the benefits of an integrated approach to multiprotocol file sharing, intelligent file tiering, federated search and active archiving; how to simplify and reduce the need for backup without the risk of losing availability; and the economic benefits of an integrated architecture approach that leads to lowering TCSO by 35% or more.

Big Data, Big Content, and Aligning Your Storage Strategy

Hitachi Vantara

Application architecture for cloud

Marco Parenzan

Similaire à Big Data Analytics on AWS - Carlos Conde - AWS Summit Paris (20)

Big Data & The Cloud

Big Data on AWS

16h30 p duff-big-data-final

BDT101 Big Data with Amazon Elastic MapReduce - AWS re: Invent 2012

Data Without Limit - Dr. Werner Vogels - AWS Summit 2012 Australia

Etendez votre datacenter avec aws v4

Don't be Hadooped when looking for Big Data ROI

Nagios Conference 2012 - Dave Josephsen - 2002 called they want there rrd she...

Architecting Virtualized Infrastructure for Big Data

Big Data and the Cloud a Best Friend Story

Cetas Analytics as a Service for Predictive Analytics

Cetas Predictive Analytics Prezo

Optimizing data mining process using graphic processors

IBM Smart Camp: Philippe Souidi on Big Data

Hive + Amazon EMR + S3 = Elastic big data SQL analytics processing in the cloud

Software Development Engineers Ireland

The Magic of Symbiotic Security

Analytics in the Cloud

Big Data, Big Content, and Aligning Your Storage Strategy

Application architecture for cloud

Plus de Amazon Web Services

Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS. In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.

Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...

Amazon Web Services

La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup. Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti. Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.

Big Data per le Startup: come creare applicazioni Big Data in modalità Server...

Amazon Web Services

Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi

Esegui pod serverless con Amazon EKS e AWS Fargate

Amazon Web Services

Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.

Costruire Applicazioni Moderne con AWS

Amazon Web Services

L’utilizzo dei container è in continua crescita. Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili. I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!

Come spendere fino al 90% in meno con i container e le istanze spot

Amazon Web Services

In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th. Event Agenda : Open banking so far (short recap) • PSD2, OB UK, OB Australia, OB LATAM, OB Israel Intro to Open Finance marketplace • Scope • Features • Tech overview and Demo The role of the Cloud The Future of APIs • Complying with regulation • Monetizing data / APIs • Business models • Time to market One platform for all: a Strategic approach Q&A

Open banking as a service

Amazon Web Services

Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc. AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta. Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.

Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...

Amazon Web Services

Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity. AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet. Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.

OpsWorks Configuration Management: automatizza la gestione e i deployment del...

Amazon Web Services

Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.

Microsoft Active Directory su AWS per supportare i tuoi Windows Workloads

Amazon Web Services

Computer Vision con AWS

Amazon Web Services

Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti. Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi. La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.

Database Oracle e VMware Cloud on AWS i miti da sfatare

Amazon Web Services

Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti. Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire. Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito. In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.

Crea la tua prima serverless ledger-based app con QLDB e NodeJS

Amazon Web Services

Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline. Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.

API moderne real-time per applicazioni mobili e web

Amazon Web Services

Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi. La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali. In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.

Database Oracle e VMware Cloud™ on AWS: i miti da sfatare

Amazon Web Services

Tools for building your MVP on AWS

Amazon Web Services

How to Build a Winning Pitch Deck

Amazon Web Services

Building a web application without servers

Amazon Web Services

Fundraising Essentials

Amazon Web Services

AWS_HK_StartupDay_Building Interactive websites while automating for efficien...

Amazon Web Services

Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.

Introduzione a Amazon Elastic Container Service

Amazon Web Services

Plus de Amazon Web Services (20)

Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...

Big Data per le Startup: come creare applicazioni Big Data in modalità Server...

Esegui pod serverless con Amazon EKS e AWS Fargate

Costruire Applicazioni Moderne con AWS

Come spendere fino al 90% in meno con i container e le istanze spot

Open banking as a service

Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...

OpsWorks Configuration Management: automatizza la gestione e i deployment del...

Microsoft Active Directory su AWS per supportare i tuoi Windows Workloads

Computer Vision con AWS

Database Oracle e VMware Cloud on AWS i miti da sfatare

Crea la tua prima serverless ledger-based app con QLDB e NodeJS

API moderne real-time per applicazioni mobili e web

Database Oracle e VMware Cloud™ on AWS: i miti da sfatare

Tools for building your MVP on AWS

How to Build a Winning Pitch Deck

Building a web application without servers

Fundraising Essentials

AWS_HK_StartupDay_Building Interactive websites while automating for efficien...

Introduzione a Amazon Elastic Container Service

Dernier

AXA XL - Insurer Innovation Award Americas 2024

The Digital Insurer

Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc

Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

apidays

Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Jeffrey Haguewood

Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...

Zilliz

In the thrilling conclusion to 2023, ransomware groups had a banner year, really outdoing themselves in the "make everyone's life miserable" department. LockBit 3.0 took gold in the hacking olympics, followed by the plucky upstarts Clop and ALPHV/BlackCat. Apparently, 48% of organizations were feeling left out and decided to get in on the cyber attack action. Business services won the "most likely to get digitally mugged" award, with education and retail nipping at their heels. Hackers expanded their repertoire beyond boring old encryption to the much more exciting world of extortion. The US, UK and Canada took top honors in the "countries most likely to pay up" category. Bitcoins were the currency of choice for discerning hackers, because who doesn't love untraceable money?

Ransomware_Q4_2023. The report. [EN].pdf

Overkill Security

AWS Community Day CPH - Three problems of Terraform

Andrey Devyatkin

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Juan lago vázquez

Real Time Object Detection Using Open CV

Khem

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

Zilliz

Accelerating FinTech Innovation: Unleashing API Economy and GenAI Vasa Krishnan, Chief Technology Officer - FinResults Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

apidays

EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER

MadyBayot

Modernizing Securities Finance: The cloud-native prime brokerage platform transforming capital markets. Madhu Subbu, Managing Director, Head of Securities Finance Engineering Apidays Singapore 2024: Connecting Customers, Business and Technology (April 17 & 18, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays Singapore 2024 - Modernizing Securities Finance by Madhu Subbu

apidays

Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

sammart93

Whatsapp Number Escorts Call girls 8617370543 Available 24x7 Navi Mumbai Call Girls Service Offer Genuine VIP Model Escorts Call Girls in Your Budget. Navi Mumbai Call Girls Service Provide Real Call Girls Number. Make Your Sexual Pleasure Memorable with Our Navi Mumbai Call Girls at Affordable Price. Top VIP Escorts Call Girls, High Profile Independent Escorts Call Girls, Housewife Women Escorts Call Girl, College Girls Escorts Call Girls, Russian Escorts Call girls Service in Your Budget.

Navi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot Model

Deepika Singh

Building Digital Trust in a Digital Economy Veronica Tan, Director - Cyber Security Agency of Singapore Apidays Singapore 2024: Connecting Customers, Business and Technology (April 17 & 18, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...

apidays

The value of a flexible API Management solution for Open Banking Steve Melan, Manager for IT Innovation and Architecture - State's and Saving's Bank of Luxembourg Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - The value of a flexible API Management solution for O...

apidays

Created by Mozilla Research in 2012 and now part of Linux Foundation Europe, the Servo project is an experimental rendering engine written in Rust. It combines memory safety and concurrency to create an independent, modular, and embeddable rendering engine that adheres to web standards. Stewardship of Servo moved from Mozilla Research to the Linux Foundation in 2020, where its mission remains unchanged. After some slow years, in 2023 there has been renewed activity on the project, with a roadmap now focused on improving the engine’s CSS 2 conformance, exploring Android support, and making Servo a practical embeddable rendering engine. In this presentation, Rakhi Sharma reviews the status of the project, our recent developments in 2023, our collaboration with Tauri to make Servo an easy-to-use embeddable rendering engine, and our plans for the future to make Servo an alternative web rendering engine for the embedded devices industry. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://ossna2024.sched.com/event/1aBNF/a-year-of-servo-reboot-where-are-we-now-rakhi-sharma-igalia

A Year of the Servo Reboot: Where Are We Now?

Igalia

Manulife - Insurer Transformation Award 2024

The Digital Insurer

Dernier (20)

AXA XL - Insurer Innovation Award Americas 2024

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...

Ransomware_Q4_2023. The report. [EN].pdf

AWS Community Day CPH - Three problems of Terraform

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Real Time Object Detection Using Open CV

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER

Apidays Singapore 2024 - Modernizing Securities Finance by Madhu Subbu

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

Navi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot Model

Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...

Apidays New York 2024 - The value of a flexible API Management solution for O...

A Year of the Servo Reboot: Where Are We Now?

Manulife - Insurer Transformation Award 2024

Big Data Analytics on AWS - Carlos Conde - AWS Summit Paris

1. BIG DATA ANALYTICS WITH AWS Carlos Conde │Solutions Architect

2. BIG DATA When innovation is required to collect, store, analyze, and share your data

3. YOU DON’T HAVE THE CHOICE…

4. 27 TB per day Large Hadron Collider – CERN

5. BIGGER IS BETTER

6. The more data you collect The more VALUE you can derive from it

7. Big Data Verticals Social Media/Advertising Oil & Gas Retail Life Sciences Financial Services Security Network/Gaming User Monte Anti-virus Demographics Targeted Recommendations Carlo Advertising Simulations Seismic Genome Fraud Usage Analysis Analysis Detection analysis Image and Transaction Risk Video Analysis Analysis Image In-game Processing Recognition metrics

8.  2.7 Zetabytes in 2012  Over 90% will be unstructured

9. VOLUME VELOCITY VARIETY

10. COLLECT │ STORE │ ANALYZE │ SHARE

11. COLLECT │ STORE │ ANALYZE │ SHARE

12.

13.

14. AWS IMPORT / EXPORT

15. COLLECT │ STORE │ ANALYZE │ SHARE

16. AMAZON S3

17. AMAZON DYNAMODB

18. HBase on AMAZON EMR

19. COLLECT │ STORE │ ANALYZE │ SHARE

20. GPU GRAPHICS PROCESSING UNIT

21.

22. CLUSTER GPU QUADRUPLE EXTRA LARGE Intel Xeon X5570, quad-core 2x Nehalem architecture NVIDIA Tesla Fermi 2x M2050 GPUs 22 GB of memory – 1.7 TB of storage $2.1 PER HOUR

23.

24. PARALELIZATION

25. ON A SINGLE INSTANCE COST: 4h x $2.1 = $8.4 RENDERING TIME: 4h

26. ON MULTIPLE INSTANCES COST: 2 x 2h x $2.1 = $8.4 RENDERING TIME:

27. "Hadoop is a reliable storage and data analysis system" HDFS MapReduce

28. Deploying a Hadoop cluster is hard http://eddie.niese.net/20090313/dont-pity-incompetence/

29. AMAZON EMR HADOOP + AWS

30.

31. Doing analytics in Eclipse is wrong…

32. PIG

33. A real Pig script (used at Twitter)

34.

35.

36.

37. USE THE RIGHT TOOL FOR THE RIGHT JOB RDBMS Hadoop  Interactive Reporting  Affordable (<1sec) Storage/Compute  Multistep Transactions  Structured or Not (Agility)  Lots of Updates/Deletes  Resilient Auto Scalability

38. Data Warehouse (Batch Processing) Data Warehouse Data Warehouse (Steady State) (Steady State) Shrink to Expand to 9 instances 25 instances

39.

40. OPERATIONAL HADOOP EXPERIENCE Operated over a million Hadoop clusters last year

41. COLLECT │ STORE │ ANALYZE │ SHARE

42. PUBLIC DATA SETS http://aws.amazon.com/publicdatasets

43.

44.

45.

46. COLLECT │ STORE │ ANALYZE │ SHARE

47. INNOVATE

48. « Want to increase innovation? Lower the cost of failure » Joi Ito

49. AWS LOWERS THE COST OF INNOVATION Testing a new idea is cheap

Big Data Analytics on AWS - Carlos Conde - AWS Summit Paris

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (7)

Similaire à Big Data Analytics on AWS - Carlos Conde - AWS Summit Paris

Similaire à Big Data Analytics on AWS - Carlos Conde - AWS Summit Paris (20)

Plus de Amazon Web Services

Plus de Amazon Web Services (20)

Dernier

Dernier (20)

Big Data Analytics on AWS - Carlos Conde - AWS Summit Paris